Your data is (not) my data: The role of social value orientation in sharing data about others

The personal data consumers share with companies on a daily basis often also involves other people. However, prior research has focused almost exclusively on how consumers make decisions about their own data. In this research, we explore how consumers’ social value orientation impacts their decisions regarding data about others. In contrast to the notion of proselfs as “selfish” decision-makers, across four studies we find that proselfs are less likely than prosocials to share data about others with third parties. We show that this effect arises because proselfs feel less ownership over data they hold about others than prosocials, which in turn reduces their willingness to share it. Overall, this work contributes to literature on social value orientation as well as privacy decision-making and helps marketers and policy makers in designing interdependent privacy choice contexts

Tolerance of Atmospheric Ammonia by Laboratory Mice

A novel preference chamber with four inter-connected compartments was designed and built to test the tolerance of atmospheric ammonia by laboratory mice. The preference chamber incorporated a novel tracking system using an infra-red sensor at each end of each tunnel, which monitored all journeys through the tunnels and their direction. An experiment was successfully undertaken with four batches, each of four mice. Each batch was housed in the chamber for 4 days and given the choice between ammonia concentrations of nominally 0, 25, 50 and 100 ppm after initial familiarization. The results showed that there were two motivations acting on mouse behavior. The mice made extensive use of the whole chamber once they had been trained to use the tunnels, at least 2000 movements between compartments for each group over 48 h. The mice clearly preferred to be in the upper two compartments of the top tier of the chamber rather than in the lower compartments. The mice did not exhibit a clear preference for or aversion to ammonia, which implies that their short- term tolerance of ammonia at potentially noxious concentrations may not be in their long-term interest

The lethal response to Cdk1 inhibition depends on sister chromatid alignment errors generated by KIF4 and isoform 1 of PRC1

Cyclin-dependent kinase 1 (Cdk1) is absolutely essential for cell division. Complete ablation of Cdk1 precludes the entry of G2 phase cells into mitosis, and is early embryonic lethal in mice. Dampening Cdk1 activation, by reducing gene expression or upon treatment with cell-permeable Cdk1 inhibitors, is also detrimental for proliferating cells, but has been associated with defects in mitotic progression, and the formation of aneuploid daughter cells. Here, we used a large-scale RNAi screen to identify the human genes that critically determine the cellular toxicity of Cdk1 inhibition. We show that Cdk1 inhibition leads to fatal sister chromatid alignment errors and mitotic arrest in the spindle checkpoint. These problems start early in mitosis and are alleviated by depletion of isoform 1 of PRC1 (PRC1-1), by gene ablation of its binding partner KIF4, or by abrogation of KIF4 motor activity. Our results show that, normally, Cdk1 activity must rise above the level required for mitotic entry. This prevents KIF4-dependent PRC1-1 translocation to astral microtubule tips and safeguards proper chromosome congression. We conclude that cell death in response to Cdk1 inhibitors directly relates to chromosome alignment defects generated by insufficient repression of PRC1-1 and KIF4 during prometaphase

Neural Predictive Control of Broiler Chicken Growth

Active control of the growth of broiler chickens has potential benefits for farmers in terms of improved production efficiency, as well as for animal welfare in terms of improved leg health. In this work, a differential recurrent neural network (DRNN) was identified from experimental data to represent broiler chicken growth using a recently developed nonlinear system identification algorithm. The DRNN model was then used as the internal model for nonlinear model predicative control (NMPC) to achieve a group of desired growth curves. The experimental results demonstrated that the DRNN model captured the underlying dynamics of the broiler growth process reasonably well. The DRNN based NMPC was able to specify feed intakes in real time so that the broiler weights accurately followed the desired growth curves ranging from $-12$ to +12% of the standard curve. The overall mean relative error between the desired and achieved broiler weight was 1.8% for the period from day 12 to day 51